{
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      " [ 7.87028266]\n",
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      " [ 9.38578209]\n",
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      " [14.24283036]\n",
      " [15.2469204 ]\n",
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      " [18.02496983]\n",
      " [18.93810195]\n",
      " [20.0698032 ]]\n"
     ]
    }
   ],
   "source": [
    "#生成二次函数的点\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    " \n",
    " \n",
    "def fix_seed(seed=1):\n",
    "    # reproducible\n",
    "    np.random.seed(seed)\n",
    " \n",
    "# make up data建立数据\n",
    "fix_seed(1)\n",
    "x_data = np.linspace(-5, 5, 100)[:, np.newaxis]  #水平轴-7~10\n",
    "print(x_data)\n",
    "#np.random.shuffle(x_data)\n",
    "noise = np.random.normal(0, 0.1, x_data.shape)\n",
    "y_data = np.square(x_data) - 5 + noise\n",
    "print(y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 1) (100, 1)\n",
      "(100, 1) (100, 1)\n",
      "[[-5.        ]\n",
      " [-4.8989899 ]\n",
      " [-4.7979798 ]\n",
      " [-4.6969697 ]\n",
      " [-4.5959596 ]\n",
      " [-4.49494949]\n",
      " [-4.39393939]\n",
      " [-4.29292929]\n",
      " [-4.19191919]\n",
      " [-4.09090909]\n",
      " [-3.98989899]\n",
      " [-3.88888889]\n",
      " [-3.78787879]\n",
      " [-3.68686869]\n",
      " [-3.58585859]\n",
      " [-3.48484848]\n",
      " [-3.38383838]\n",
      " [-3.28282828]\n",
      " [-3.18181818]\n",
      " [-3.08080808]\n",
      " [-2.97979798]\n",
      " [-2.87878788]\n",
      " [-2.77777778]\n",
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      " [-2.27272727]\n",
      " [-2.17171717]\n",
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      " [-1.96969697]\n",
      " [-1.86868687]\n",
      " [-1.76767677]\n",
      " [-1.66666667]\n",
      " [-1.56565657]\n",
      " [-1.46464646]\n",
      " [-1.36363636]\n",
      " [-1.26262626]\n",
      " [-1.16161616]\n",
      " [-1.06060606]\n",
      " [-0.95959596]\n",
      " [-0.85858586]\n",
      " [-0.75757576]\n",
      " [-0.65656566]\n",
      " [-0.55555556]\n",
      " [-0.45454545]\n",
      " [-0.35353535]\n",
      " [-0.25252525]\n",
      " [-0.15151515]\n",
      " [-0.05050505]\n",
      " [ 0.05050505]\n",
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      " [ 0.85858586]\n",
      " [ 0.95959596]\n",
      " [ 1.06060606]\n",
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      " [ 1.36363636]\n",
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      " [ 1.56565657]\n",
      " [ 1.66666667]\n",
      " [ 1.76767677]\n",
      " [ 1.86868687]\n",
      " [ 1.96969697]\n",
      " [ 2.07070707]\n",
      " [ 2.17171717]\n",
      " [ 2.27272727]\n",
      " [ 2.37373737]\n",
      " [ 2.47474747]\n",
      " [ 2.57575758]\n",
      " [ 2.67676768]\n",
      " [ 2.77777778]\n",
      " [ 2.87878788]\n",
      " [ 2.97979798]\n",
      " [ 3.08080808]\n",
      " [ 3.18181818]\n",
      " [ 3.28282828]\n",
      " [ 3.38383838]\n",
      " [ 3.48484848]\n",
      " [ 3.58585859]\n",
      " [ 3.68686869]\n",
      " [ 3.78787879]\n",
      " [ 3.88888889]\n",
      " [ 3.98989899]\n",
      " [ 4.09090909]\n",
      " [ 4.19191919]\n",
      " [ 4.29292929]\n",
      " [ 4.39393939]\n",
      " [ 4.49494949]\n",
      " [ 4.5959596 ]\n",
      " [ 4.6969697 ]\n",
      " [ 4.7979798 ]\n",
      " [ 4.8989899 ]\n",
      " [ 5.        ]]\n"
     ]
    }
   ],
   "source": [
    "# X = np.sort(x_data,axis=0)\n",
    "# y = np.sort(y_data,axis=0)\n",
    "\n",
    "#维度转换\n",
    "print(x_data.shape,y_data.shape)\n",
    "X = x_data.reshape(-1,1)\n",
    "print(X.shape,y_data.shape)\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
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   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot input data\n",
    "plt.scatter(X , y_data)  #将数据绘制图一元二次函数的数据集点\n",
    "plt.title('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 50)                100       \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 50)                2550      \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 51        \n",
      "=================================================================\n",
      "Total params: 2,701\n",
      "Trainable params: 2,701\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#建立模型\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "model1 = Sequential()\n",
    "model1.add(Dense(units=50,input_dim=1,activation=\"relu\"))\n",
    "model1.add(Dense(units=50,activation=\"relu\"))\n",
    "model1.add(Dense(units=1,activation=\"linear\"))\n",
    "model1.compile(optimizer=\"adam\",loss=\"mean_squared_error\")  \n",
    "model1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "100/100 [==============================] - 0s 120us/step - loss: 0.1213\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 2/200\n",
      "100/100 [==============================] - 0s 140us/step - loss: 0.1106\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 3/200\n",
      "100/100 [==============================] - 0s 120us/step - loss: 0.1019\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 4/200\n",
      "100/100 [==============================] - 0s 130us/step - loss: 0.1122\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 5/200\n",
      "100/100 [==============================] - 0s 390us/step - loss: 0.1135\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 6/200\n",
      "100/100 [==============================] - 0s 200us/step - loss: 0.1020\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 7/200\n",
      "100/100 [==============================] - 0s 110us/step - loss: 0.1109\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 8/200\n",
      "100/100 [==============================] - 0s 190us/step - loss: 0.1033\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 9/200\n",
      "100/100 [==============================] - 0s 130us/step - loss: 0.1011\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 10/200\n",
      "100/100 [==============================] - 0s 270us/step - loss: 0.1006\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 11/200\n",
      "100/100 [==============================] - 0s 340us/step - loss: 0.1014\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 12/200\n",
      "100/100 [==============================] - 0s 150us/step - loss: 0.1054\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 13/200\n",
      "100/100 [==============================] - 0s 130us/step - loss: 0.1057\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 14/200\n",
      "100/100 [==============================] - 0s 130us/step - loss: 0.0986\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 15/200\n",
      "100/100 [==============================] - 0s 241us/step - loss: 0.1039\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 16/200\n",
      "100/100 [==============================] - 0s 120us/step - loss: 0.1053\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 17/200\n",
      "100/100 [==============================] - 0s 190us/step - loss: 0.0926\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 18/200\n",
      "100/100 [==============================] - 0s 150us/step - loss: 0.0995\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 19/200\n",
      "100/100 [==============================] - 0s 230us/step - loss: 0.0967\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 20/200\n",
      "100/100 [==============================] - 0s 131us/step - loss: 0.0891\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 21/200\n",
      "100/100 [==============================] - 0s 280us/step - loss: 0.0926\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 22/200\n",
      "100/100 [==============================] - 0s 230us/step - loss: 0.0891\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 23/200\n",
      "100/100 [==============================] - 0s 130us/step - loss: 0.0909\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 24/200\n",
      "100/100 [==============================] - 0s 221us/step - loss: 0.1080\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 25/200\n",
      "100/100 [==============================] - 0s 260us/step - loss: 0.1029\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 26/200\n",
      "100/100 [==============================] - 0s 280us/step - loss: 0.0892\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 27/200\n",
      "100/100 [==============================] - 0s 380us/step - loss: 0.0924\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 28/200\n",
      "100/100 [==============================] - 0s 250us/step - loss: 0.0928\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 29/200\n",
      "100/100 [==============================] - ETA: 0s - loss: 0.0685\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b - 0s 170us/step - loss: 0.0861\n",
      "Epoch 30/200\n",
      "100/100 [==============================] - 0s 180us/step - loss: 0.0846\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 31/200\n",
      "100/100 [==============================] - 0s 260us/step - loss: 0.0826\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 32/200\n",
      "100/100 [==============================] - 0s 245us/step - loss: 0.0842\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 33/200\n",
      "100/100 [==============================] - 0s 180us/step - loss: 0.0865\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 34/200\n",
      "100/100 [==============================] - 0s 490us/step - loss: 0.0970\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 35/200\n",
      "100/100 [==============================] - 0s 785us/step - loss: 0.0811\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 36/200\n",
      "100/100 [==============================] - 0s 200us/step - loss: 0.0835\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 37/200\n",
      "100/100 [==============================] - 0s 190us/step - loss: 0.0823\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 38/200\n",
      "100/100 [==============================] - 0s 200us/step - loss: 0.0786\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 39/200\n",
      "100/100 [==============================] - 0s 260us/step - loss: 0.0787\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 40/200\n",
      "100/100 [==============================] - 0s 170us/step - loss: 0.0807\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 41/200\n",
      "100/100 [==============================] - 0s 190us/step - loss: 0.0778\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 42/200\n",
      "100/100 [==============================] - 0s 400us/step - loss: 0.0740\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 43/200\n",
      "100/100 [==============================] - 0s 330us/step - loss: 0.0852\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 44/200\n",
      "100/100 [==============================] - 0s 230us/step - loss: 0.0801\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 45/200\n",
      "100/100 [==============================] - 0s 140us/step - loss: 0.0772\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 46/200\n",
      "100/100 [==============================] - 0s 170us/step - loss: 0.0768\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 47/200\n",
      "100/100 [==============================] - 0s 140us/step - loss: 0.0732\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 48/200\n",
      "100/100 [==============================] - 0s 130us/step - loss: 0.0742\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 49/200\n",
      "100/100 [==============================] - 0s 140us/step - loss: 0.0723\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 50/200\n",
      "100/100 [==============================] - 0s 140us/step - loss: 0.0716\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 51/200\n",
      "100/100 [==============================] - 0s 120us/step - loss: 0.0791\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 52/200\n",
      "100/100 [==============================] - 0s 305us/step - loss: 0.0773\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 53/200\n",
      "100/100 [==============================] - 0s 200us/step - loss: 0.0703\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 54/200\n",
      "100/100 [==============================] - 0s 140us/step - loss: 0.0773\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 55/200\n",
      "100/100 [==============================] - 0s 220us/step - loss: 0.0702\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 56/200\n",
      "100/100 [==============================] - 0s 120us/step - loss: 0.0724\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 57/200\n",
      "100/100 [==============================] - 0s 190us/step - loss: 0.0807\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 58/200\n",
      "100/100 [==============================] - 0s 160us/step - loss: 0.0711\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 59/200\n",
      "100/100 [==============================] - 0s 180us/step - loss: 0.0673\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 60/200\n",
      "100/100 [==============================] - 0s 150us/step - loss: 0.0687\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 61/200\n",
      "100/100 [==============================] - 0s 220us/step - loss: 0.0652\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 62/200\n",
      "100/100 [==============================] - 0s 160us/step - loss: 0.0656\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 63/200\n",
      "100/100 [==============================] - 0s 130us/step - loss: 0.0660\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 64/200\n",
      "100/100 [==============================] - 0s 140us/step - loss: 0.0656\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 65/200\n",
      "100/100 [==============================] - 0s 140us/step - loss: 0.0708\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 66/200\n",
      "100/100 [==============================] - 0s 320us/step - loss: 0.0634\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 67/200\n",
      "100/100 [==============================] - 0s 120us/step - loss: 0.0710\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 68/200\n",
      "100/100 [==============================] - 0s 130us/step - loss: 0.0602\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 69/200\n",
      "100/100 [==============================] - 0s 130us/step - loss: 0.0699\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 70/200\n",
      "100/100 [==============================] - 0s 170us/step - loss: 0.0705\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 71/200\n",
      "100/100 [==============================] - 0s 260us/step - loss: 0.0664\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 72/200\n",
      "100/100 [==============================] - 0s 200us/step - loss: 0.0639\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 73/200\n",
      "100/100 [==============================] - 0s 160us/step - loss: 0.0602\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 74/200\n",
      "100/100 [==============================] - 0s 190us/step - loss: 0.0580\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 75/200\n",
      "100/100 [==============================] - 0s 150us/step - loss: 0.0608\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 76/200\n",
      "100/100 [==============================] - ETA: 0s - loss: 0.0911\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b - 0s 190us/step - loss: 0.0599\n",
      "Epoch 77/200\n",
      "100/100 [==============================] - 0s 130us/step - loss: 0.0595\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 78/200\n",
      "100/100 [==============================] - 0s 210us/step - loss: 0.0576\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 79/200\n",
      "100/100 [==============================] - 0s 160us/step - loss: 0.0601\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 80/200\n",
      "100/100 [==============================] - 0s 180us/step - loss: 0.0735\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 81/200\n",
      "100/100 [==============================] - 0s 270us/step - loss: 0.0577\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 82/200\n",
      "100/100 [==============================] - 0s 170us/step - loss: 0.0722\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 83/200\n",
      "100/100 [==============================] - 0s 180us/step - loss: 0.0638\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 84/200\n",
      "100/100 [==============================] - 0s 390us/step - loss: 0.0570\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 85/200\n",
      "100/100 [==============================] - 0s 230us/step - loss: 0.0606\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 86/200\n",
      "100/100 [==============================] - 0s 130us/step - loss: 0.0541\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 87/200\n",
      "100/100 [==============================] - 0s 240us/step - loss: 0.0571\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 88/200\n",
      "100/100 [==============================] - 0s 110us/step - loss: 0.0585\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 89/200\n",
      "100/100 [==============================] - 0s 130us/step - loss: 0.0563\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 90/200\n",
      "100/100 [==============================] - 0s 130us/step - loss: 0.0600\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 91/200\n",
      "100/100 [==============================] - 0s 200us/step - loss: 0.0518\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 92/200\n",
      "100/100 [==============================] - 0s 120us/step - loss: 0.0581\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 93/200\n",
      "100/100 [==============================] - 0s 150us/step - loss: 0.0553\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 94/200\n",
      "100/100 [==============================] - 0s 210us/step - loss: 0.0515\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 95/200\n",
      "100/100 [==============================] - 0s 180us/step - loss: 0.0544\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 96/200\n",
      "100/100 [==============================] - 0s 210us/step - loss: 0.0512\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 97/200\n",
      "100/100 [==============================] - 0s 180us/step - loss: 0.0533\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 98/200\n",
      "100/100 [==============================] - 0s 160us/step - loss: 0.0513\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 99/200\n",
      "100/100 [==============================] - 0s 130us/step - loss: 0.0519\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 100/200\n",
      "100/100 [==============================] - 0s 290us/step - loss: 0.0490\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 101/200\n",
      "100/100 [==============================] - 0s 180us/step - loss: 0.0567\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 102/200\n",
      "100/100 [==============================] - 0s 110us/step - loss: 0.0519\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 103/200\n",
      "100/100 [==============================] - 0s 170us/step - loss: 0.0492\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 104/200\n",
      "100/100 [==============================] - 0s 150us/step - loss: 0.0479\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 105/200\n",
      "100/100 [==============================] - 0s 240us/step - loss: 0.0502\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 106/200\n",
      "100/100 [==============================] - 0s 210us/step - loss: 0.0506\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 107/200\n",
      "100/100 [==============================] - 0s 150us/step - loss: 0.0512\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 108/200\n",
      "100/100 [==============================] - 0s 190us/step - loss: 0.0639\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 109/200\n",
      "100/100 [==============================] - 0s 140us/step - loss: 0.0463\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 110/200\n",
      "100/100 [==============================] - 0s 280us/step - loss: 0.0543\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 111/200\n",
      "100/100 [==============================] - 0s 120us/step - loss: 0.0517\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 112/200\n",
      "100/100 [==============================] - 0s 140us/step - loss: 0.0477\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 113/200\n",
      "100/100 [==============================] - 0s 220us/step - loss: 0.0493\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 114/200\n",
      "100/100 [==============================] - 0s 100us/step - loss: 0.0499\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 115/200\n",
      "100/100 [==============================] - 0s 150us/step - loss: 0.0497\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 116/200\n",
      "100/100 [==============================] - 0s 121us/step - loss: 0.0519\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 117/200\n",
      "100/100 [==============================] - 0s 110us/step - loss: 0.0625\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 118/200\n",
      "100/100 [==============================] - 0s 101us/step - loss: 0.0692\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 119/200\n",
      "100/100 [==============================] - 0s 181us/step - loss: 0.1351\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 120/200\n",
      "100/100 [==============================] - 0s 130us/step - loss: 0.0779\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 121/200\n",
      "100/100 [==============================] - 0s 170us/step - loss: 0.0538\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 122/200\n",
      "100/100 [==============================] - 0s 190us/step - loss: 0.0647\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 123/200\n",
      "100/100 [==============================] - 0s 190us/step - loss: 0.0520\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 124/200\n",
      "100/100 [==============================] - 0s 300us/step - loss: 0.0439\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 125/200\n",
      "100/100 [==============================] - 0s 120us/step - loss: 0.0464\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 126/200\n",
      "100/100 [==============================] - 0s 180us/step - loss: 0.0433\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 127/200\n",
      "100/100 [==============================] - 0s 100us/step - loss: 0.0451\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 128/200\n",
      "100/100 [==============================] - 0s 140us/step - loss: 0.0404\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 129/200\n",
      "100/100 [==============================] - 0s 340us/step - loss: 0.0429\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 130/200\n",
      "100/100 [==============================] - 0s 280us/step - loss: 0.0455\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 131/200\n",
      "100/100 [==============================] - 0s 170us/step - loss: 0.0374\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 132/200\n",
      "100/100 [==============================] - 0s 270us/step - loss: 0.0450\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 133/200\n",
      "100/100 [==============================] - 0s 120us/step - loss: 0.0452\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 134/200\n",
      "100/100 [==============================] - 0s 210us/step - loss: 0.0385\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 135/200\n",
      "100/100 [==============================] - 0s 170us/step - loss: 0.0397\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 136/200\n",
      "100/100 [==============================] - 0s 200us/step - loss: 0.0378\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 137/200\n",
      "100/100 [==============================] - 0s 220us/step - loss: 0.0412\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 138/200\n",
      "100/100 [==============================] - 0s 200us/step - loss: 0.0420\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 139/200\n",
      "100/100 [==============================] - 0s 210us/step - loss: 0.0423\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 140/200\n",
      "100/100 [==============================] - 0s 120us/step - loss: 0.0591\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 141/200\n",
      "100/100 [==============================] - 0s 130us/step - loss: 0.0498\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 142/200\n",
      "100/100 [==============================] - 0s 150us/step - loss: 0.0418\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 143/200\n",
      "100/100 [==============================] - 0s 190us/step - loss: 0.0495\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 144/200\n",
      "100/100 [==============================] - 0s 230us/step - loss: 0.0492\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 145/200\n",
      "100/100 [==============================] - 0s 150us/step - loss: 0.0417\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 146/200\n",
      "100/100 [==============================] - 0s 191us/step - loss: 0.0389\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 147/200\n",
      "100/100 [==============================] - 0s 240us/step - loss: 0.0445\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 148/200\n",
      "100/100 [==============================] - 0s 141us/step - loss: 0.0355\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 149/200\n",
      "100/100 [==============================] - 0s 180us/step - loss: 0.0417\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 150/200\n",
      "100/100 [==============================] - 0s 130us/step - loss: 0.0385\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 151/200\n",
      "100/100 [==============================] - 0s 230us/step - loss: 0.0634\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 152/200\n",
      "100/100 [==============================] - 0s 130us/step - loss: 0.0400\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 153/200\n",
      "100/100 [==============================] - 0s 141us/step - loss: 0.0404\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 154/200\n",
      "100/100 [==============================] - 0s 160us/step - loss: 0.0400\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 155/200\n",
      "100/100 [==============================] - 0s 150us/step - loss: 0.0418\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 156/200\n",
      "100/100 [==============================] - ETA: 0s - loss: 0.0725\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b - 0s 190us/step - loss: 0.0584\n",
      "Epoch 157/200\n",
      "100/100 [==============================] - 0s 161us/step - loss: 0.0371\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 158/200\n",
      "100/100 [==============================] - 0s 190us/step - loss: 0.0469\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 159/200\n",
      "100/100 [==============================] - 0s 150us/step - loss: 0.0354\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 160/200\n",
      "100/100 [==============================] - 0s 140us/step - loss: 0.0388\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 161/200\n",
      "100/100 [==============================] - 0s 160us/step - loss: 0.0308\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 162/200\n",
      "100/100 [==============================] - 0s 130us/step - loss: 0.0449\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 163/200\n",
      "100/100 [==============================] - 0s 190us/step - loss: 0.0362\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 164/200\n",
      "100/100 [==============================] - 0s 180us/step - loss: 0.0413\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 165/200\n",
      "100/100 [==============================] - 0s 210us/step - loss: 0.0340\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 166/200\n",
      "100/100 [==============================] - 0s 150us/step - loss: 0.0332\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 167/200\n",
      "100/100 [==============================] - 0s 190us/step - loss: 0.0318\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 168/200\n",
      "100/100 [==============================] - 0s 220us/step - loss: 0.0312\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 169/200\n",
      "100/100 [==============================] - 0s 170us/step - loss: 0.0313\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 170/200\n",
      "100/100 [==============================] - 0s 190us/step - loss: 0.0303\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 171/200\n",
      "100/100 [==============================] - 0s 180us/step - loss: 0.0306\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 172/200\n",
      "100/100 [==============================] - 0s 200us/step - loss: 0.0340\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 173/200\n",
      "100/100 [==============================] - 0s 140us/step - loss: 0.0517\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 174/200\n",
      "100/100 [==============================] - 0s 140us/step - loss: 0.0360\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 175/200\n",
      "100/100 [==============================] - 0s 110us/step - loss: 0.0309\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 176/200\n",
      "100/100 [==============================] - 0s 240us/step - loss: 0.0352\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 177/200\n",
      "100/100 [==============================] - 0s 130us/step - loss: 0.0319\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 178/200\n",
      "100/100 [==============================] - 0s 160us/step - loss: 0.0298\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 179/200\n",
      "100/100 [==============================] - 0s 130us/step - loss: 0.0296\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 180/200\n",
      "100/100 [==============================] - 0s 160us/step - loss: 0.0281\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 181/200\n",
      "100/100 [==============================] - 0s 120us/step - loss: 0.0287\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 182/200\n",
      "100/100 [==============================] - 0s 110us/step - loss: 0.0304\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 183/200\n",
      "100/100 [==============================] - 0s 150us/step - loss: 0.0316\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 184/200\n",
      "100/100 [==============================] - 0s 110us/step - loss: 0.0272\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 185/200\n",
      "100/100 [==============================] - 0s 130us/step - loss: 0.0280\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 186/200\n",
      "100/100 [==============================] - 0s 140us/step - loss: 0.0337\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 187/200\n",
      "100/100 [==============================] - 0s 210us/step - loss: 0.0300\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 188/200\n",
      "100/100 [==============================] - 0s 150us/step - loss: 0.0322\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 189/200\n",
      "100/100 [==============================] - 0s 251us/step - loss: 0.0330\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 190/200\n",
      "100/100 [==============================] - 0s 180us/step - loss: 0.0389\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 191/200\n",
      "100/100 [==============================] - 0s 120us/step - loss: 0.0406\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 192/200\n",
      "100/100 [==============================] - 0s 110us/step - loss: 0.0282\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 193/200\n",
      "100/100 [==============================] - 0s 100us/step - loss: 0.0288\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 194/200\n",
      "100/100 [==============================] - 0s 140us/step - loss: 0.0258\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 195/200\n",
      "100/100 [==============================] - 0s 300us/step - loss: 0.0318\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 196/200\n",
      "100/100 [==============================] - 0s 180us/step - loss: 0.0295\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 197/200\n",
      "100/100 [==============================] - 0s 140us/step - loss: 0.0264\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 198/200\n",
      "100/100 [==============================] - 0s 150us/step - loss: 0.0303\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 199/200\n",
      "100/100 [==============================] - 0s 120us/step - loss: 0.0284\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 200/200\n",
      "100/100 [==============================] - 0s 200us/step - loss: 0.0270\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x1c4e33dbf88>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#训练模型\n",
    "model1.fit(X,y_data,epochs=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[19.744678  ]\n",
      " [18.87144   ]\n",
      " [17.998207  ]\n",
      " [17.12497   ]\n",
      " [16.251738  ]\n",
      " [15.378499  ]\n",
      " [14.505266  ]\n",
      " [13.632029  ]\n",
      " [12.758796  ]\n",
      " [11.885558  ]\n",
      " [11.012323  ]\n",
      " [10.155483  ]\n",
      " [ 9.391917  ]\n",
      " [ 8.628351  ]\n",
      " [ 7.864785  ]\n",
      " [ 7.1266403 ]\n",
      " [ 6.4620867 ]\n",
      " [ 5.8037124 ]\n",
      " [ 5.145338  ]\n",
      " [ 4.486964  ]\n",
      " [ 3.890752  ]\n",
      " [ 3.3388286 ]\n",
      " [ 2.7869027 ]\n",
      " [ 2.234979  ]\n",
      " [ 1.6830547 ]\n",
      " [ 1.1311302 ]\n",
      " [ 0.6178944 ]\n",
      " [ 0.13666832]\n",
      " [-0.2955736 ]\n",
      " [-0.70276564]\n",
      " [-1.0965137 ]\n",
      " [-1.4849412 ]\n",
      " [-1.8509502 ]\n",
      " [-2.1941557 ]\n",
      " [-2.5319593 ]\n",
      " [-2.8697636 ]\n",
      " [-3.205598  ]\n",
      " [-3.4591146 ]\n",
      " [-3.7013319 ]\n",
      " [-3.9403822 ]\n",
      " [-4.164968  ]\n",
      " [-4.361994  ]\n",
      " [-4.5308743 ]\n",
      " [-4.6922345 ]\n",
      " [-4.802182  ]\n",
      " [-4.8447227 ]\n",
      " [-4.8561187 ]\n",
      " [-4.852195  ]\n",
      " [-4.84067   ]\n",
      " [-4.8291454 ]\n",
      " [-4.817621  ]\n",
      " [-4.8060966 ]\n",
      " [-4.794572  ]\n",
      " [-4.7830477 ]\n",
      " [-4.7715235 ]\n",
      " [-4.705677  ]\n",
      " [-4.5613537 ]\n",
      " [-4.3800244 ]\n",
      " [-4.2471538 ]\n",
      " [-4.128347  ]\n",
      " [-3.9857032 ]\n",
      " [-3.8025324 ]\n",
      " [-3.5790248 ]\n",
      " [-3.2965605 ]\n",
      " [-2.9931145 ]\n",
      " [-2.657645  ]\n",
      " [-2.2788959 ]\n",
      " [-1.892633  ]\n",
      " [-1.5056746 ]\n",
      " [-1.1187161 ]\n",
      " [-0.7292998 ]\n",
      " [-0.30294642]\n",
      " [ 0.17479631]\n",
      " [ 0.6620649 ]\n",
      " [ 1.1493336 ]\n",
      " [ 1.6393821 ]\n",
      " [ 2.1585023 ]\n",
      " [ 2.715553  ]\n",
      " [ 3.2826984 ]\n",
      " [ 3.886474  ]\n",
      " [ 4.4928045 ]\n",
      " [ 5.099136  ]\n",
      " [ 5.7258396 ]\n",
      " [ 6.388565  ]\n",
      " [ 7.0600595 ]\n",
      " [ 7.8194084 ]\n",
      " [ 8.63511   ]\n",
      " [ 9.450812  ]\n",
      " [10.266516  ]\n",
      " [11.082217  ]\n",
      " [11.897921  ]\n",
      " [12.713625  ]\n",
      " [13.529326  ]\n",
      " [14.34503   ]\n",
      " [15.16073   ]\n",
      " [15.976437  ]\n",
      " [16.792137  ]\n",
      " [17.60784   ]\n",
      " [18.423542  ]\n",
      " [19.239246  ]]\n"
     ]
    }
   ],
   "source": [
    "#预测\n",
    "y_predict = model1.predict(X)\n",
    "print(y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#每训练100次 画一次图\n",
    "fig2 = plt.figure(figsize=(7,5))\n",
    "plt.scatter(X, y_data)\n",
    "plt.plot(X,y_predict,'g')\n",
    "plt.title('y-x 100 predict')\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, y_data)\n",
    "plt.plot(X,y_predict,'g')\n",
    "plt.title('y-x 200 predict')\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X,y_predict,'r')\n",
    "plt.scatter(X, y_data)\n",
    "plt.title('y-x 400 predict')\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "训练400次 拟合得很好了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model.pkl']"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 保存模型\n",
    "#from sklearn.externals import joblib\n",
    "import joblib\n",
    "joblib.dump(model1,'model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#加载模型\n",
    "import joblib\n",
    "model2 = joblib.load('model.pkl')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-4.        ]\n",
      " [-3.89915966]\n",
      " [-3.79831933]\n",
      " [-3.69747899]\n",
      " [-3.59663866]\n",
      " [-3.49579832]\n",
      " [-3.39495798]\n",
      " [-3.29411765]\n",
      " [-3.19327731]\n",
      " [-3.09243697]\n",
      " [-2.99159664]\n",
      " [-2.8907563 ]\n",
      " [-2.78991597]\n",
      " [-2.68907563]\n",
      " [-2.58823529]\n",
      " [-2.48739496]\n",
      " [-2.38655462]\n",
      " [-2.28571429]\n",
      " [-2.18487395]\n",
      " [-2.08403361]\n",
      " [-1.98319328]\n",
      " [-1.88235294]\n",
      " [-1.78151261]\n",
      " [-1.68067227]\n",
      " [-1.57983193]\n",
      " [-1.4789916 ]\n",
      " [-1.37815126]\n",
      " [-1.27731092]\n",
      " [-1.17647059]\n",
      " [-1.07563025]\n",
      " [-0.97478992]\n",
      " [-0.87394958]\n",
      " [-0.77310924]\n",
      " [-0.67226891]\n",
      " [-0.57142857]\n",
      " [-0.47058824]\n",
      " [-0.3697479 ]\n",
      " [-0.26890756]\n",
      " [-0.16806723]\n",
      " [-0.06722689]\n",
      " [ 0.03361345]\n",
      " [ 0.13445378]\n",
      " [ 0.23529412]\n",
      " [ 0.33613445]\n",
      " [ 0.43697479]\n",
      " [ 0.53781513]\n",
      " [ 0.63865546]\n",
      " [ 0.7394958 ]\n",
      " [ 0.84033613]\n",
      " [ 0.94117647]\n",
      " [ 1.04201681]\n",
      " [ 1.14285714]\n",
      " [ 1.24369748]\n",
      " [ 1.34453782]\n",
      " [ 1.44537815]\n",
      " [ 1.54621849]\n",
      " [ 1.64705882]\n",
      " [ 1.74789916]\n",
      " [ 1.8487395 ]\n",
      " [ 1.94957983]\n",
      " [ 2.05042017]\n",
      " [ 2.1512605 ]\n",
      " [ 2.25210084]\n",
      " [ 2.35294118]\n",
      " [ 2.45378151]\n",
      " [ 2.55462185]\n",
      " [ 2.65546218]\n",
      " [ 2.75630252]\n",
      " [ 2.85714286]\n",
      " [ 2.95798319]\n",
      " [ 3.05882353]\n",
      " [ 3.15966387]\n",
      " [ 3.2605042 ]\n",
      " [ 3.36134454]\n",
      " [ 3.46218487]\n",
      " [ 3.56302521]\n",
      " [ 3.66386555]\n",
      " [ 3.76470588]\n",
      " [ 3.86554622]\n",
      " [ 3.96638655]\n",
      " [ 4.06722689]\n",
      " [ 4.16806723]\n",
      " [ 4.26890756]\n",
      " [ 4.3697479 ]\n",
      " [ 4.47058824]\n",
      " [ 4.57142857]\n",
      " [ 4.67226891]\n",
      " [ 4.77310924]\n",
      " [ 4.87394958]\n",
      " [ 4.97478992]\n",
      " [ 5.07563025]\n",
      " [ 5.17647059]\n",
      " [ 5.27731092]\n",
      " [ 5.37815126]\n",
      " [ 5.4789916 ]\n",
      " [ 5.57983193]\n",
      " [ 5.68067227]\n",
      " [ 5.78151261]\n",
      " [ 5.88235294]\n",
      " [ 5.98319328]\n",
      " [ 6.08403361]\n",
      " [ 6.18487395]\n",
      " [ 6.28571429]\n",
      " [ 6.38655462]\n",
      " [ 6.48739496]\n",
      " [ 6.58823529]\n",
      " [ 6.68907563]\n",
      " [ 6.78991597]\n",
      " [ 6.8907563 ]\n",
      " [ 6.99159664]\n",
      " [ 7.09243697]\n",
      " [ 7.19327731]\n",
      " [ 7.29411765]\n",
      " [ 7.39495798]\n",
      " [ 7.49579832]\n",
      " [ 7.59663866]\n",
      " [ 7.69747899]\n",
      " [ 7.79831933]\n",
      " [ 7.89915966]\n",
      " [ 8.        ]]\n",
      "[[ 7.81217268]\n",
      " [ 5.89756788]\n",
      " [ 5.16314384]\n",
      " [ 4.13486658]\n",
      " [ 4.36851343]\n",
      " [ 2.06983654]\n",
      " [ 3.39814559]\n",
      " [ 1.47060762]\n",
      " [ 1.35653953]\n",
      " [ 0.43848126]\n",
      " [ 0.68070442]\n",
      " [-1.67359835]\n",
      " [-1.3775775 ]\n",
      " [-1.96089943]\n",
      " [-1.73415334]\n",
      " [-3.36281196]\n",
      " [-3.39057114]\n",
      " [-4.21443941]\n",
      " [-4.20521895]\n",
      " [-4.36539629]\n",
      " [-5.61725401]\n",
      " [-4.88438555]\n",
      " [-5.37541748]\n",
      " [-5.92409356]\n",
      " [-6.05370309]\n",
      " [-7.15444779]\n",
      " [-7.16214422]\n",
      " [-7.83636152]\n",
      " [-7.74986099]\n",
      " [-7.57784183]\n",
      " [-8.395615  ]\n",
      " [-8.4345889 ]\n",
      " [-8.74588845]\n",
      " [-8.97065734]\n",
      " [-9.00909245]\n",
      " [-8.78487901]\n",
      " [-9.42194167]\n",
      " [-8.81048087]\n",
      " [-8.14185232]\n",
      " [-8.62445846]\n",
      " [-9.09478791]\n",
      " [-9.42573666]\n",
      " [-9.31821583]\n",
      " [-8.04078633]\n",
      " [-8.78364916]\n",
      " [-9.02925271]\n",
      " [-8.49666146]\n",
      " [-7.4030184 ]\n",
      " [-8.2337557 ]\n",
      " [-7.8055853 ]\n",
      " [-7.76411581]\n",
      " [-7.87000247]\n",
      " [-8.02447568]\n",
      " [-7.36688942]\n",
      " [-7.01532912]\n",
      " [-6.31589679]\n",
      " [-5.86770552]\n",
      " [-5.47929749]\n",
      " [-5.43936861]\n",
      " [-4.7565679 ]\n",
      " [-5.1729761 ]\n",
      " [-3.74564417]\n",
      " [-3.67157689]\n",
      " [-3.61271424]\n",
      " [-2.73469722]\n",
      " [-2.51169307]\n",
      " [-1.38270589]\n",
      " [-0.642888  ]\n",
      " [ 0.25605301]\n",
      " [-0.9485836 ]\n",
      " [-0.36565552]\n",
      " [ 0.73124281]\n",
      " [ 1.71090618]\n",
      " [ 2.73672156]\n",
      " [ 3.14454158]\n",
      " [ 2.68404804]\n",
      " [ 4.27080873]\n",
      " [ 5.5869977 ]\n",
      " [ 6.05749493]\n",
      " [ 7.11322789]\n",
      " [ 7.43117051]\n",
      " [ 8.27240537]\n",
      " [ 9.31685248]\n",
      " [10.29972253]\n",
      " [11.08530903]\n",
      " [11.95746351]\n",
      " [12.4947656 ]\n",
      " [13.97135375]\n",
      " [14.81629514]\n",
      " [16.31327666]\n",
      " [17.3614814 ]\n",
      " [17.88842596]\n",
      " [18.66236812]\n",
      " [19.60514578]\n",
      " [21.23109609]\n",
      " [22.17319443]\n",
      " [23.09811059]\n",
      " [24.44768643]\n",
      " [25.2920757 ]\n",
      " [27.14761781]\n",
      " [27.79190073]\n",
      " [29.86491962]\n",
      " [30.7119499 ]\n",
      " [32.0848692 ]\n",
      " [32.53883742]\n",
      " [34.48953551]\n",
      " [36.11401101]\n",
      " [36.62610853]\n",
      " [38.34941317]\n",
      " [39.89873083]\n",
      " [40.61610358]\n",
      " [42.90081817]\n",
      " [44.62723257]\n",
      " [45.2556456 ]\n",
      " [47.36226543]\n",
      " [48.05277716]\n",
      " [50.23183507]\n",
      " [51.00589816]\n",
      " [53.95743225]\n",
      " [55.20445027]]\n"
     ]
    }
   ],
   "source": [
    "#加载新数据\n",
    "#生成二次函数点\n",
    "def fix_seed(seed=1):\n",
    "    # reproducible\n",
    "    np.random.seed(seed)\n",
    " \n",
    "fix_seed(1)\n",
    "X2 = np.linspace(-6, 6, 120)[:, np.newaxis]+[2]  #水平轴 + 偏移\n",
    "print(X2)\n",
    "#np.random.shuffle(x_data)\n",
    "noise = np.random.normal(1, 0.5, X2.shape)\n",
    "y_data2 = np.square(X2) - 10 + noise\n",
    "print(y_data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot input data\n",
    "plt.scatter(X2 , y_data2)  #将数据绘制图一元二次函数的数据集点\n",
    "plt.title('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#预测\n",
    "y2_predict= model2.predict(X2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pp8PNN0NT0+Afc4AU/EREal2xMzzd4bvfDR9nnw2zZ0NDvOFHy54iIrUqOqD29s8VL8PTPcz2vvvdsM/3y1/GHvhAMz8RkdqUOttLZ7AZnu7w1a/C1VfD+efDT38KdeUx5yqPUYiISGllO7EhMpgMz97ekNhy9dUhAP7sZ2UT+EDBT0SkNuWa1Q0mw7OnBz77Wfj5z+Gii+Cqq8BsYI9VJAp+IiK1KNusbujYgR9Z1NUFn/hEKGL/znfgP/6j7AIfKPiJiNSmTPV806+Frz01sMDX2QlnnRXall1xBcycWZaBD5TwIiJSm6LgFtX2Dbaeb/Nm+NjH4K67QiH7V75SuLEWgYKfiEitSS5oHzpm8PV87e0wbVro0flf/xUyO8ucgp+ISC1JLXFYvyJ8DwMLgBs3wsknwx//CP/933DuuQUbajFpz09EpJYU8sii9evD6esPPAA33lgxgQ808xMRqS2FOrLo7bdD4Hv8cZgzJ/TrrCCa+YmI1JJCHFm0ejVMnQrLlsHtt1dc4AMFPxGR2jLYI4tefx2OOgr+9jeYPz/s91UgLXuKiNSSwZQ4rFoVDqBdtQruvhv+4R+KOtRiUvATEakFqeUN/a3pe/nlEPhWr4aFC+GII4o31hJQ8BMRqXaDLW94/vmwx/fOO7BoEUyZUryxloj2/EREqlUhzuv729/gwx+GTZvg97+visAHmvmJiFSnQpzX99RTYcZnBn/4AxxwQEGHGCfN/EREqtFgz+tbujQktDQ0hO4tVRT4QMFPRKQ6Dea8vocfDsktQ4bA/ffDhAmFH1/MFPxERKrRQM/r+9Of4NhjYcSIEPj23LN4Y4yR9vxERKrJ1pKGFYABvu26xtbsh9Ted18oWh8zJnw9enQpRhwLBT8RkWqxXZKLszUADh2bvbbvd7+D004LM71Fi+Bd7yrRoOMRW/AzsxbgfqA5MY7b3P07ZjYCmAOMB14CZrj72rjGKSJSMdImuSQC39eeyny/O++EGTNgv/3CmXy77FLUYZaDOPf8tgBHu/tBwMHACWZ2GHARsNjd9wYWJ74XEZFsls1NLHWmkS355dZbwwnsBx0UljprIPBBjMHPg42JbxsTHw6cCsxOXD4bmBbD8EREKke03JlJpuSXG2+Es84KheuLFsHw4cUZXxmKNdvTzOrN7HHgTeBed38Y2M3dXwNIfN41zjGKiJS9bDV9mUoafvEL+NSnQveWhQthp52KO8YyE2vwc/cedz8YGANMMbO8qyjN7DwzW2JmS1avXl28QYqIlLtsy5rpsjt/9jP47GfhuOPgrrtghx2KO74yVBZ1fu6+DvgDcALwhpmNAkh8fjPDfWa5+2R3nzxy5MiSjVVEpOxkPKB27PaB70c/gi9+MZQ03HkntLamv2+Viy34mdlIMxuW+LoVOAb4GzAfOCdxs3OAO+MZoYhIhcj3gNrLL4d/+ZeQ4HLbbdDcXLoxlpk46/xGAbPNrJ4QhOe6+2/M7CFgrpl9FngFOCPGMYqIlL9cB9S6w8yZcOmlcPbZMHt26NlZw8zdc9+qzE2ePNmXLFkS9zBERMqPO1x8MXz/+/DpT8O110J9fdyjKgkze9TdJ6e7riz2/EREZACi8/pmDgufl83te717WOb8/vfh/PPhuutqJvDlUtvzXhGRSpXrdHZ3+PKX4ac/ha98JSS6mMU33jKjmZ+ISCVKV9sXnc7e2wtf+EIIfF//ugJfGgp+IiKVJFrqzNTKbN0K+Od/hmuugYsugiuvVOBLQ8ueIiKVYrtTG1K4w6IGeHBWSHK57DIFvgwU/EREKkW2NmbusLAbHt4A3/qWAl8OWvYUEakU2dqY/aUFHu6Ab3wjFLMr8GWl4CciUikytTFbvgPcszrU8f3gBwp8eVDwExGpFOnamL1UB7e9AcceCz//uQJfnhT8REQqxcQZ4ZSGoWMBg827wq+3wH77h16djY1xj7BiKOFFRKTcLZu7fd/OcceFQ2hbd4AFC2ruPL7BUvATESln6Tq53PllWLg7vPQS3HcfjBsX6xArkYKfiEg5S1fe8Lt18OCbMGsWfPCD8YyrwmnPT0SkXC2bu30nl6e64MFOmNwEn/tcPOOqAgp+IiLlKFruTPZ6D9zZAePqYcae8YyrSij4iYiUo9TlzvZemNMOrQZnD4fjZ8Y2tGqg4CciUo6Su7n0OtzWARsczmyDj/902yntMiAKfiIi5Si5m8u9W+DvPfDRFthvvAJfASj4iYiUkz5HFhks64S/dMKUJpgyNNT4yaCp1EFEpFyk1vS91g0LNsO76+GMveC472jWVyA5g5+ZfQm4yd3XlmA8IiK1KznJZVMiwaXN4DN7wTeejndsVSafZc93AY+Y2VwzO8FMXVNFRIoiSnLpSSS4bEokuPS+Hu+4qlDO4Ofu3wb2Bq4HzgWeM7P/MDMVmYiIFEK0z4eH7+/dAi/1wEktsHt95qOMZMDySnhxdwdeT3x0A8OB28zsB0Ucm4hI9Yv2+aJOLk90wsOdcGgTHNQUjjBSkkvB5bPndwFwDvAWcB1wobt3mVkd8BzwzeIOUUSkiiXv873aExJcxtfDcc3h6KKplyjJpQjyyfbcBZju7i8nX+juvWZ2UnGGJSJS5bYeU5SY8W1MJLjsYPCxVqirg689Fe8Yq1jO4OfuGefb7v5sYYcjIlIDUksaogSXdofPDIEhddrnKzIVuYuIlFpq386Fm+HlHjilFUbVa5+vBBT8RERKLblv59JOeKQLPtAEBzaGfb6Tr9Y+X5HF1uHFzMYCvyTUEfYCs9z9/5nZCGAOMB54CZhR7AL7eUtXceXC5by6roPdh7Vy4fETmDZpdDGfUkRq2dAxYa9vVQ/ctRneUw/HJBJctM9XEnHO/LqBr7v7vsBhwBfNbD/gImCxu+8NLE58XzTzlq7i4tufZNW6DhxYta6Di29/knlLVxXzaUWkisxbuoojrriPPS66iyOuuC/368fUS2BzU0hw2dHg9FZobtNSZwnFFvzc/TV3fyzx9QbgWWA0cCowO3Gz2cC0Yo7jyoXL6ejq6XNZR1cPVy5cXsynFZEq0e830MvmwsKZcPNa6Eh0cBn1bi11llhZ7PmZ2XhgEvAwsJu7vwYhQAK7FvO5X13X0a/LRUSS9esNdJTlOed5WNEDp7bC2B1UyxeD2IOfme0A/Br4qru/04/7nWdmS8xsyerVqwf8/LsPa+3X5SIiyfr1BnrxpfDweni0Cw5vggMaQ9bn4kuLPEpJFWvwM7NGQuC7yd1vT1z8hpmNSlw/Cngz3X3dfZa7T3b3ySNHjhzwGC48fgKtjfV9x0VYushr7V5EalK0z+cZrk/7Bvqpl0KCy571MLV52+XJ2Z9SErEFv8TpENcDz7r7VUlXzSe0UyPx+c5ijmPapNFcPv1ARid+UY2trWWV/CIiaSXv86XT2ljPhcdP2HbBsrkwcwLMbYehBqe3QV3SATkqaC+5OGd+RwCfBI42s8cTHx8BrgCONbPngGMT3xfVtEmj+fNFRzN6WOt27+KU/CIiqdLt80VGD2vl8ukHbiuXWjYX7vgyXP8CbEkkuLQmBT4VtMcitjo/d/8TYaKVztRSjiWi5BcRyWXe0lUZZ3wG/Pmio/teuPhSmL8WVvaEnp27JW2zqHF1bGJPeCknmZJcHLT/JyJblzsz6fMaEp3Rt/gFeKwLPtgE+zcm3dpCQbsCXywU/JKkS36JaP9PRLItd/bZ54tKGp78O9y9GfZqgKOa+95B+3yxUvBLkpr8kkr7fyK1LdsWyOXTD2Ra/Z/DbO/2z8GaTTC3IyS4TG/tm+Cifb7YKfiliJJfMm1Gav9PpPbkKmsYPaw1BL7oRPZuD5mdnQ5npSS4qHF1WVDwyyDT/l+dmZY+RWpI3mUN0TFF7qGWb1UvnNYKu6YkuGifrywo+GWQaf+vx117fyI1JO+yhqhQfUkXPN4FRzbBvkkJLlrqLCsKfhlE+3/1tv0CaEdXD1+d87gyQEVqQKatjqisYdqk0SHBxerg5W743WbYOyXBRUudZUfBL4tpk0bT65lW+ZUBKlLN8m5fFmV2ruuCWztgeF1IcDELs73p12qpswwp+OWQq8G1MkBFqk+/2pctvhQ62kOCS5fDma3QYmD1mu2VMQW/HLLV/kWUASpSXfrVvmzdCvjNZng1keAyMvF64b0KfGUstvZmlSL6Bb9y4fKM7wJ1/JFIdZi3dFXWv/W07cuebIMn3oEPN8N7kxJcVMRe1jTzy0NU+/fjMw/W8UciVSrXUiekeaP7hz/A/Ddh32b4cNO2y5XZWfYU/PpBxx+JVK9sS52Q5piihT+Dk46B4cCZu0HbzoAps7NCKPj1U67jj1QCIVKZsu3db7fP99cb4TNfge6ekODi66C7A6bPUmZnhdCe3wBl+0OJZoGwbc9QRMrXvKWrqDOjJ01p0+hhrX33+dzhC1+CV7vh462wS2IrpKsjZH4q8FUEzfwGSCUQItUh2utLF/jSnsh+2u7w6PpQxL5PY987RF1epOwp+A2QSiBEqkOmvb56s+1PZP/x+TD/dXhvQ2hflkoZnhVDwW+Ach1/BGqCLVLOog4umbI7e937blvc+m24ZR3sUgfTEh1ckinDs6Io+A1CthIIUBNskXLV77KG9na47gXoTXRwaU4JfMrwrDgKfgWQqwm29v5Eyku/yhrc4XOfgzd64fRW2Dnlja6OKapICn4Fkq0JtorgRcpDrqVOSFPWcNVVcPPN8KWzYL+d+t5YS50VS6UOBbT7sNaMf1QqfxCJV7TUmW3Gt11Zw733wjcvhIN2guG/hYYR0NAKHWtDcsvUSzTjq1Ca+RVQrgxQLYGKxKffHVxefBHOmB4aVX/EQ0unjrdVzF4lFPwKKJ8MUJU/iJTWgJY6N22CE46CLZtgRgs0Je3nR8XsUtEU/Aosuf1ZOg7a/xMpkXyyOqOlzq2Bzx2mHwvPvxISXEakeZlUMXvFU/ArkmxLoGqCLVIa/V7qBPjBD+Ceh+DoZtgrQ1qEitkrnoJfkeRaAlUTbJHimrd0Vf+WOgH+61/h4otg/wY4Ik0HF1CGZ5VQtmcRTZs0mmmTRrPHRXdtdwJERFmgIoUXLXdmsl1WJ8BvroZ/uQJ2rYNT0nRwAbB6FbNXCc38SkBNsEVKK9tyZ9qlzo0b4XPfDBmdZ7b1TXCJNLbCadco8FWJWIOfmf3CzN40s6eSLhthZvea2XOJz8PjHGMhqAm2SGll+3vabqnziTlwxGh4Ywt8rBWGp3lZVPuyqhP3zO8G4ISUyy4CFrv73sDixPcVTU2wRUojKmvItM0welhr38C3bC58459g2TtwTDO8J81OkNqXVaVYg5+73w+8nXLxqcDsxNezgWklHVSRqAm2SHHlKmtIu9z5nxfCoo1wYAN8IE2Ci5JbqlbcM790dnP31wASn3eNeTwFpSbYIsWRbZ8vbWbnc8/B/7wC76qDk9MkuGips6qVY/DLi5mdZ2ZLzGzJ6tWr4x5Ov6gJtkjh5OrgYtC3iB3godlw5IFQZyHBpTFN4NNSZ1Urx+D3hpmNAkh8fjPdjdx9lrtPdvfJI0eOLOkACyFbBqiK4EXy0+9z+QAevwU++3l4cwuc0QrDUl4GtdRZE8ox+M0Hzkl8fQ5wZ4xjKRo1wRYZvAF1cLn4Anh2CxzXDHukJLhoqbNmxF3q8CvgIWCCma00s88CVwDHmtlzwLGJ76tOPhmgWgIVSW9AzaoBFiyA362GiY1waGqCi2mps4bE2uHF3T+e4aqpJR1ITKIOMNn+iNUBRqSvAZ3LB7B8OZx9Joyqh5Na0iS4qF9nLSnHZc+ak88SqPqAioTA9/W5T/R/qXP9ejjhaPDNcGbr9gku2uerOertWWrL5oazwNav3HoS9LRJYZnlyoXLsy7jaBYotSya8fVkyJSGMOO78PgJff8+envhU5+CV16FT7bB0JT3/OrXWZM08yulZXNhwQWwfgXg4fOCC2DZ3JznAEaUCCO1Kldyy3bn8kW+eCbMnw/HtcD4NO/3vVeBrwYp+JXS4kvDKdDJUk6FVh9Qkb7ySW7Zbqlz2Vz40QFwVhtccxsc3AhTGtPfWXt9NUnBr5Qynf6cdLn6gIpsk08dX71Z36zOaIXl+acrP3QAABijSURBVJfgjg7YvQ4+mibBBbTXV8MU/Eopeof5Rg/8qh22eN/LE9QHVCTIp47v/844qO9S5+JLYUM7zOkIiS1ntkFDmsCnmr6apuBXSlMvCe80Nzo81w23d0B9S8Z3nrn6gCoDVKrVgOv4ANatCH9ba3tDB5edMhxRpJq+mqZsz1KK/tDaLoU1L8Ddm+GFQ7P+AU6bNJqvzXk84/XKAJVqM+A6viiT+vebw5vLj7TAu9O8xGmpU9DMr/QmzgjvOH/bAWceD7MXwCltYXN+2dy0d9FJ8FILotneV+c83v86vmif7y8vwgOdMKkRJicnuCRWT7TUKQma+cVl2VzYbynsWQ93dcCOL0L7BeG6lD/MC4+fkPOdcNQKbbsaJ5EKkM9sD9LU8W2tm10Bb/aEBJfR9WHWZ0kBb+olCnjSh4JfXBZfCr2b4Yw2uGET3NoBn7JwecofafSHriJ4qVa5ElsgzVJnNNvr6oAOh1vaodlgRmtSgkuiX6dICi17xiUqb2g2+Mc22MHg5g544aW0S6C5MkAjSoSRSjNv6aqsb+ogw1JnVDfb6/Drdljv2ye4qIZPMlDwi0vyH+UOdfCJIeF/48Z2WPHy1s4vqfKpAwSdCSiVIVruzCZjVmf0BvK+LfBCD5zYAuOSFrOU2CJZmGfpk1cpJk+e7EuWLIl7GP2TvGQTea0nLIEOq4NPD4HdxmVdssmVCh5J2+9QJEbzlq7KuYzf2lifPuhFfnQAPPgi3NYBhzTCyUlvCLXPJ4CZPeruk9Ndp5lfXCbOCFlnQ8duu2xUfSjIfas3FMG/9Ur4A585LO1SaD6t0ECzQCkv+XRtAbIHvmVz4eW1cGcHjKkPsz4Is73p16qGT3JS8ItTVPaQHADf0wDTW+GVHvh1B6x9hdQm2JF8l0BB5RASv3xLGSCsVmQNfHO+BL98rW+CS+sIlTFI3hT8ykHU+SWyfyOc0ALLu+E3myFamk5pgg35J8JAmAFq9idxyHe2BxmSW5Ld81245W14x8PZfDsmXsaahijwSd5U6lAOoj/YqF4J4NAm2NQbCnZ3MDg6sayzfuWgzgT82pzH+eqcx7UPKCWVTykD5Lk/fcfz8GIPnNwCY5JewjI1jhdJQ8GvXEycET5mDgMSM72jmkMf0Ac6Q0bolCZoHd43USZaDgWmTZrBtEmjsxYMR+lNqgmUUsgnsQWyJLekvtHrnAoPdobuLYc09b2tyhqkH7TsWW6S/4DN4KQWmNAQ+oAuTxTu5jgTMNoLzEX7gFJM+S51ZixlSD38eflL8O9Xw767w0nD+95WZQ3STwp+5SZ1/6/O4PRWGN8Mv94Ez6xOf7+UJZ9pk0bnlQgTtUXTXqAU0rylq/j63Cdy9uj88ZkHb3/6enQQ7e2f2/ZGr70X5rRDi8FH3oGWtpDggqlfpwyIgl+56VMCkfjDPvM6eOw12GdCOKNsVZoXlDRLPiqFkDhEM76eLDXE+c32Eno91PJt8FAKtEMddLwN3R0wfZbKGmRAVOReKZbNhdv/Ha5+DjqAc9tgt0Rga2zN+M43ec/F2Lbnl4kSYWSg8t3fS3scUeRHB/QNfAC/2wwPd8KpLXBw6j7fWPXulIyyFbkr4aUSRO+G6YBPDoH/3gT/0w6fboP3jM/ayWLapNFbA1k+L05KhJGByPdUhpxlDKkZm491hsB3aNP2gS/d7UXypGXPShA18AUYXgefbAtTuF9ugWNvyXvJJ6oJzLUX2NHVw9fnPqFlUMlbPqUM9Wa5u7ZY0kvSS91w1+Zw7NdxzenvowxPGSAFv0qQ+u52ZD2c0wbeA0ccBl95D8wcCt8dET5nORgX8tsL7HHXPqDkFHVtyaeU4f/OOCh74FtwQfidBnirB+Z2wIg6+PgImPJPfRPBQBmeMija86sE6fZBADp2hOtehw09cFZbaI0WybIPCPnvz4D2AaWvouwjJ/+Or+6B2e3h68/sCP90bfg9TtPcQYkukk22PT8Fv0qQ7gSIxlZoaIXVb4X9vzW9oSfofo3bbpNHMkB/9mqyLllJTcj39wX6+TsTNXd4owd+2R7WpD7VBiMbYOa6QY9bapNOdah06cofTr4aOtaGtO9zh4QTIW7rCAkCkagVWpaTIaKC+HozstEhubWtP02pIUspQ6ro9xMPR3rNbod6wrL+yHrt6UnRaOZXyZKXijod5raHQz2PaYYjogSBlIWpDMuhRXtHLxWvP78bkFLKkG2pMnlFY2U33NQOTQbnDAl7fTmW7kVyqciZn5mdYGbLzex5M7so7vGUpeRuME0GH2+D/Rtg0RZYFJ0GkfLmJs3JEND/45E0C6wN+XRqSdanlCG1PVnqsVxRFvMr3WHpvtXCIc4j6tS1RYquLIOfmdUDPwVOBPYDPm5m+8U7qjKUeiBuQ+IswPc1wp87w3FIvWlm9hlqo/pzPBKoM0y1y6dTC4S1BUiz1JlcohNJfvO1fiU8nwh8OyaW74fVhUdU1xYpsnItcp8CPO/uLwKY2S3AqcAzsY6qHEWnQURmDoOPtoR30X/qhM0eAmJ90p5ejn2U6MUrn2zQqCYw+X5S2QqWCZypAH39irBk/0xnOLB5ZB18ItG2DLTPJyVRrsFvNJCc278SODT5BmZ2HnAewLhx40o3snI3dEx4cZmaCID3boEt7TCjLSyN5lkbFXWGyWe/p8dd5wRWiYJk/0b7fBmLIAx+/0JYmRhTD2e3hYbVoNo9KZmyXPZk20pKsj5/Se4+y90nu/vkkSNHlmhYFSB5H/DwZjilJRz8+ctNUDeq3/so+e4Fpp4TqKXQytKfbM6snVrSNaZO5g73dcCCzfCe+jDjiwKf9vmkhMoy29PMPgDMdPfjE99fDODul6e7fc1me2aSmmFXdyJc/FPYdVf47W9hv4Ftnw4k60+zwPKTvKxZb0aPe17F6pBHpm+mhgwA3R6C3rIumNQYlue3Lseb6vmk4CquyN3MGoD/BaYCq4BHgLPd/el0t1fwy8OSJXDyydDRAZd/ATbPH1CnjCj7L1cSRCR6UVUgLA/9fQOTLOv/4dY3XBkC35qeUIf6ei8c1QxHNoXDmiM6nUGKoOJOdXD3bjP7ErCQUPL6i0yBT/I0eTLc8O9w7lfhC5fDh5vhw03b0s8jOdpHRS98+b6Api6HJj+GlE5/klhS5ZztpetA1Of6LrirI8zyzmqFCY19r9c+n8SgLGd+/aWZXx6iF6j29pBosKwL9qqH01qhLdr6za8gHvrf3zFSb5a9wbEUzED/j5L1uy9nss0Ov+2AJ7thXH3IOh6akmYwdKx6dErRVNyyZ38p+OUh+QXKHR7tCoeEDjE4vRXGZVgEyLM/aH9mFVoKLb7BLG/CAPtyJvt7N8zrgI0OH/8A7PMC9G7edr26t0gJKPhJ+heo13rg1nZY5zC1GQ5P2YcB+pOIMJAXXAXCwhrM8ma//y/S7fN1eegw9NdOGNkEv3kApkzRiQwSCwU/yb40taADnumGfRrg1JakZVD6nYgwmKU2BcKBm7d0FTPnP826jq5+3S/K9uzXv/myuXD3t6Dj7b6Xr+iGeZvh7V44rBV+dA0c9ql+jUekkBT8JHtSgjs80gX3JJZBP9YKYxsGvTTV38zQZGqenb+BzLgH/O+b7veo2+H3W+ChTtjJ4Ox3w/nf18xOYqfgJ0Hq0tPex8ETN297IXs1sQy63uGE4XDkTrBlLVh9OGF7AMkJg9l7MmBYWyPr2rvYXbNBYNvM+tV1HQxtbcQM1rbnN9sryMw6dQVhVQ/c2QGre+GQRjiuFS5f3//HFSkCBT/JLDUgTvkGfPcauPfhkA16auu2noswoNlgIbIOoXaXRUuWtZkq+XejdXi4LFrq7HL4Q2K2t4PBKa2wV4Pq9aSsKPhJ/1y1Pyx+ERZuDv1AT2iGAxu3JcMM4gVOgbB/Spq1CSlJLBn+h57rgoVbYE1itndsS2hRpgxOKTMKftI/UWbo6h6YvxlW9sDeDfCRlm1HzkyfNejsvUIFQoDhbY185+T9Kz4Ipi5rvrO5K+2pVPno979JrmL11T1hX/j5nnDm3kdaYM9EiUzrCDhR+3xSXhT8pH+S93V6PaSt37clRKcjEyURDXX0DVeJ8DXAouXBJMekjKDPbDA5mJTTvmGmvbvBvgmAQcyGM2UEt/eGhJZHu6CJ0B1oSlPo2KIidSljCn7SP+lmAOt7w7v+Z7rDu/5jmuG9DWnqAhnw8tdgl/iSRUEkXTDJNCMqVqBMDXSd3T20d/UO+nFTDTpDNrUWtDvxxuf+LdAJTG4MgW9IdO6e9vekvCn4Sf8l7/1E2Z4AL3SHvcDVvTC2Ho5tDmURqVpHQNOQfi+LFnNGlCx6vGFZniP6PqqFi26bKfs0rrEPOBs2Ndmpc1NIaHGHZ7th0WZY62HJ+9hmGFm/7b7a35MKoOAng5c8K+h1WNoVlsI2ecjy+4dmGF2f+f4FqBks1P5goSQHoWLN5lIVrDdqpv29l7tDh5aVPbBrHRyX2NdrHRGu71irDi1SMRT8ZPDS7Qd1JpbFHuyEjsQM4cM5gmAB9ogG2s2k0g2qMD1TyULk9Z6wr/tcN+xo4c3MYbvBST9QkJOKpeAng5d2ppCY+2wB/pqo+epw2LMePtgM764v6J5gqnKcDRZCwZY1IXcG51s9oV7v6W5oIfy/TWmCRtOenlQ8BT8pjEzNiaNej+vWwJLETLDdYfc6OCKRGFOXJggWMFOwkgNhW2MdzY31he9ks2wu3HH+tv3aZOt64Y9b4ImucKrnYU1weHOo19tKp6tLZVPwk+JLXhbt8vCi+lBnaHI83OADzXBwY5hR9DG4Eol00mVtAnkFx2xZooNR0NlcPjLN+Nb2woNbwp4twOSmcKr6kLrtH0MzP6lwCn5SfOmOTOp1WN4Nf94Cq3pD0+zDm8ILblPxlkNzSZeVmRqMkmeSqdme+WRwFm02l0u6Y4bc4dVeeLgTnuoKkfjgRvhQc9LhsvkfZCxSKRT8pPgyFUi3joCudnh+Y6gX+3tPCIIfaIL3pwmCFTLbyCeAllzqbK/DQ7B7rBNe74VG4H1N4d9+p0TQi4Ic6Lw9qToKflJ86ZbZtnthXQGvdIe9phd7oNXCXtOUpqS9Ju0z9VvybK/L4cVueLIL/tYNPcC76kLQO7ARmutCxqdKFqQGZAt+aaqTRQYgegHNNHuIEmMWXADjGmBlN9zfGWoF/7wlvDgf1gQ71cP399CLc76emAM3fBGe3wTPd4cmBF2ENxbva4KDGmFUXci61VKmyFaa+UlppZ4a8HpiT/Dp7nD9Pg1hP2rvhtA7MvUFO1PGaaUZzM/xzjtw3Xdhzi/g2fWwIfE3vKPBhAaY0Ah71Id/v4h6cEoN0rKnlKfkVPy1vaFMYlkXbHRoM9i3AQ5ohHH1MGRn6N4CXZv6PkYlzmYyLREfdDY8d09KMfra8PWaLnhyDbxYDy92QHdvqMvbswHGN8D4eti5bvu6ykr89xEpEAU/KV+pWaK9HpbvnuwKmaJdhASZvRvCrGaPBmgu0ySZfGdzmZKDkjMuN/bCyz3w9+6wh7c2cfkudWF2vE9D6K2arn4yotme1Djt+Un5GjqmbyCoM9inMXx0OvxvNyzvgme74PEuqAPG1MN7Ei/+o+tDsCmkgSxJps7m1q8I38P2900dr3uoh1zRA68kPtYk+oQ2EQL+YQ2hc87OWVrHRTTbE8lJMz+JV672W5EeD0HhhcRM6LVEcDBCA+ZxbTCmGXbeDHuNgxNnDuzFP1sbt9SZlDt0dUFnJ/x4MqxdFbIrexy6CV+37ArTfwFbtsCzi+CxW2DNW7ChN+zVvdULb/SEGS6ERJWx9aE13LsbQqZmfZbZXSrN9kS20rKnlLd8Gi+n2uzh5IFXuuHVHljVA5uTrh9RBzsZjNgB9j0c3rUf1CdmTa89DX9/CDo2Qn0b7HIAdHfDqqdhS/u2wNXj4XN30tc9gLVAV08IegNlhOXcnetgt3rYrS4EvV3S7NvlQ7M9ke0o+EnlyXdGGHGHdR5OJ3ijF1b3hJnVht7QZ9QtbKe5Qz0h+DRY+LreEh+J67ZenriuIbpN0mVDdoK9PgzjJkFTEzx4FXStS7lPyteNia+bLQS+bPt1/WH1cNo1CnwiKbTnJ5UntW4wOfsxdZ8QwmxpuMHwOti3FAPsBO4FHoTeZjioHWimcB1BbVsxep/ZsNqQiRSCgp+Ur4kzMr+oZ8yYLLGuTUnlFwNoid06Aro70nfGSfezV0udo0jM0rRyLz4zO8PMnjazXjObnHLdxWb2vJktN7Pj4xifVICpl4QgUXT9XZr0sAyZj8ZWOPH7IdANHRuea+jY7DO5iTNCWcfMdeGzAp/IgMQ183sKmA78PPlCM9sPOAvYH9gdWGRm+7inO5BMalrGZdE0S4MDka4vab68J9w/235lalamgphIScUS/Nz9WQDbPqvtVOAWd98C/N3MngemAA+VdoRSETIti0aH62bNGM0SIK2+7+wruS9pPgk4UWBLNwbt0YmUhViWPbMYDSS/xV6ZuEwkfxNnwLf+DtOv3bac2DoifERLi9NnhetTl04bW9NnTk6cEYJW64jsz93Yum1GlzqGXEuaIlIyRZv5mdki4F1prvo3d78z093SXJb27bmZnQecBzBu3LgBjVGqXLaEmWT5JpBEj5e2LjHDKRT5jkFESqpowc/djxnA3VYCY5O+HwO8muHxZwGzINT5DeC5RAYWnBTQRCpeuS17zgfOMrNmM9sD2Bv4a8xjEhGRKhNXqcNpZrYS+ABwl5ktBHD3p4G5wDPA74AvKtNTREQKLa5szzuAOzJcdxlwWWlHJCIitaTclj1FRESKTsFPRERqjoKfiIjUHAU/ERGpOQp+IiJSc6riMFszWw28HPc4+mkX4K24B1Ek+tkqT7X+XKCfrRIV6ud6t7uPTHdFVQS/SmRmSzKdMFzp9LNVnmr9uUA/WyUqxc+lZU8REak5Cn4iIlJzFPziMyvuARSRfrbKU60/F+hnq0RF/7m05yciIjVHMz8REak5Cn5lwMy+YWZuZrvEPZZCMbMrzexvZrbMzO4ws2Fxj2kwzOwEM1tuZs+b2UVxj6dQzGysmf3ezJ41s6fN7Ctxj6mQzKzezJaa2W/iHkshmdkwM7st8Tf2rJl9IO4xFYqZfS3xu/iUmf3KzFqK8TwKfjEzs7HAscArcY+lwO4FDnD3icD/AhfHPJ4BM7N64KfAicB+wMfNbL94R1Uw3cDX3X1f4DDgi1X0swF8BXg27kEUwf8Dfufu7wUOokp+RjMbDVwATHb3A4B64KxiPJeCX/x+BHwTqKrNV3e/x927E9/+BRgT53gGaQrwvLu/6O6dwC3AqTGPqSDc/TV3fyzx9QbCi+joeEdVGGY2BvgocF3cYykkM9sJ+BBwPYC7d7r7unhHVVANQKuZNQBtwKvFeBIFvxiZ2SnAKnd/Iu6xFNlngLvjHsQgjAZWJH2/kioJEMnMbDwwCXg43pEUzI8Jbyx74x5Igb0HWA38d2JJ9zozGxL3oArB3VcBPySshL0GrHf3e4rxXAp+RWZmixJr16kfpwL/BlwS9xgHKsfPFt3m3whLazfFN9JBszSXVdVM3cx2AH4NfNXd34l7PINlZicBb7r7o3GPpQgagEOA/3L3ScAmoCr2oc1sOGFVZQ9gd2CImX2iGM8Vy0nutcTdj0l3uZkdSPgPfsLMICwLPmZmU9z99RIOccAy/WwRMzsHOAmY6pVdU7MSGJv0/RiKtBQTBzNrJAS+m9z99rjHUyBHAKeY2UeAFmAnM7vR3YvyQlpiK4GV7h7N0G+jSoIfcAzwd3dfDWBmtwOHAzcW+ok084uJuz/p7ru6+3h3H0/4hT6kUgJfLmZ2AvAt4BR3b497PIP0CLC3me1hZk2EDfj5MY+pICy887oeeNbdr4p7PIXi7he7+5jE39ZZwH1VEvhIvEasMLMJiYumAs/EOKRCegU4zMzaEr+bUylSMo9mflIsPwGagXsTM9u/uPv58Q5pYNy928y+BCwkZJ/9wt2fjnlYhXIE8EngSTN7PHHZv7r7b2Mck+T2ZeCmxJuxF4FPxzyegnD3h83sNuAxwnbJUorU7UUdXkREpOZo2VNERGqOgp+IiNQcBT8REak5Cn4iIlJzFPxERKTmKPiJiEjNUfATEZGao+AnUgXM7P2JsxNbzGxI4jy0A+Iel0i5UpG7SJUws/9D6GPZSuj9eHnMQxIpWwp+IlUi0erqEWAzcLi798Q8JJGypWVPkeoxAtgB2JEwAxSRDDTzE6kSZjafcMr8HsAod/9SzEMSKVs61UGkCpjZp4Bud7/ZzOqBB83saHe/L+6xiZQjzfxERKTmaM9PRERqjoKfiIjUHAU/ERGpOQp+IiJScxT8RESk5ij4iYhIzVHwExGRmqPgJyIiNef/A76ewSWuJCUkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig3 = plt.figure(figsize=(7,5))\n",
    "plt.plot(X2,y2_predict,'r')\n",
    "plt.scatter(X,y_data,label=\"data1\")\n",
    "plt.scatter(X2, y_data2,label=\"data2\")\n",
    "plt.title('model predict')\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "模型拟合的还是旧数据的\n",
    "\n",
    "# 二次训练\n",
    "transfer learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "120/120 [==============================] - 0s 117us/step - loss: 11.7479\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 2/10\n",
      "120/120 [==============================] - 0s 100us/step - loss: 6.8885\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 3/10\n",
      "120/120 [==============================] - 0s 83us/step - loss: 3.5398\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 4/10\n",
      "120/120 [==============================] - 0s 67us/step - loss: 3.9814\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 5/10\n",
      "120/120 [==============================] - 0s 67us/step - loss: 4.1141\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 6/10\n",
      "120/120 [==============================] - 0s 83us/step - loss: 3.5760\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 7/10\n",
      "120/120 [==============================] - 0s 75us/step - loss: 2.7598\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 8/10\n",
      "120/120 [==============================] - 0s 67us/step - loss: 2.6219\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 9/10\n",
      "120/120 [==============================] - 0s 83us/step - loss: 2.4048\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 10/10\n",
      "120/120 [==============================] - 0s 108us/step - loss: 2.7150\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x1c4e6779f88>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.fit(X2,y_data2,epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#再预测\n",
    "y2_predict2 = model2.predict(X2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig4 = plt.figure(figsize=(7,5))\n",
    "plt.plot(X2,y2_predict2,'r')\n",
    "plt.scatter(X,y_data,label=\"data1\")\n",
    "plt.scatter(X2, y_data2,label=\"data2\")\n",
    "plt.title('model predict epochs = 10')\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "训练10次就可以拟合到了新数据上了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "120/120 [==============================] - 0s 300us/step - loss: 2.0014\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 2/10\n",
      "120/120 [==============================] - 0s 125us/step - loss: 2.1092\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 3/10\n",
      "120/120 [==============================] - 0s 83us/step - loss: 1.8071\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 4/10\n",
      "120/120 [==============================] - 0s 92us/step - loss: 1.6962\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 5/10\n",
      "120/120 [==============================] - 0s 75us/step - loss: 1.7166\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 6/10\n",
      "120/120 [==============================] - 0s 108us/step - loss: 1.5410\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 7/10\n",
      "120/120 [==============================] - 0s 92us/step - loss: 1.4801\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 8/10\n",
      "120/120 [==============================] - 0s 92us/step - loss: 1.4250\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 9/10\n",
      "120/120 [==============================] - 0s 108us/step - loss: 1.3523\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
      "Epoch 10/10\n",
      "120/120 [==============================] - 0s 92us/step - loss: 1.2560\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model2.fit(X2,y_data2,epochs=10)\n",
    "#再预测\n",
    "y2_predict2 = model2.predict(X2)\n",
    "fig4 = plt.figure(figsize=(7,5))\n",
    "plt.plot(X2,y2_predict2,'r')\n",
    "plt.scatter(X,y_data,label=\"data1\")\n",
    "plt.scatter(X2, y_data2,label=\"data2\")\n",
    "plt.title('model predict epochs = 20')\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  }
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